Description Usage Arguments Value Author(s) References Examples
This function calculates the activities of pathways in the whole genome with the average-based rank scoring algorithm (Yang, et al., 2011; Ma and Wang, 2013). This rank-based statistics is robust for directly comparing the activities of pathways with different gene numbers under different experimental conditions, since it produces a normalized value with the consideration of gene number in the analyzed pathways and whole genomes.
1 | AverageRankScore( featureMat, selGenes )
|
featureMat |
a numeric matrix recording the expression levels or changes of all genes in the genome at given conditions. |
selGenes |
a character vector recording a set of genes in the analyzed pathway. |
value |
a numeric vector recording the activities of interested genes (selGenes) at different conditions. |
Chuang Ma, Xiangfeng Wang
[1] Huang Yang, Chao Cheng and Wei Zhang. Average rank-based score to measure deregulation of molecular pathway gene sets. PLoS One, 2011, 6(11): e27579.
[2] Chuang Ma, Xiangfeng Wang. Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis thaliana. 2013(Submitted).
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## Not run:
##generate expression feature matrix
sampleVec1 <- c(1, 2, 3, 4, 5, 6)
sampleVec2 <- c(1, 2, 3, 4, 5, 6)
featureMat <- expFeatureMatrix( expMat1 = ControlExpMat, sampleVec1 = sampleVec1,
expMat2 = SaltExpMat, sampleVec2 = sampleVec2,
logTransformed = TRUE, base = 2,
features = "foldchange" )
##for an interested set of genes, the average-based rank score can be calculated:
genes <- rownames(featureMat)[1:100]
res <- AverageRankScore( featureMat = featureMat, selGenes = genes )
## End(Not run)
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